| import torch | |
| from constants import DIALECTS, DIALECTS_WITH_LABELS | |
| def predict_top_p(model, tokenizer, text, P=0.9): | |
| """Predict the top dialects with an accumulative confidence of at least P.""" | |
| assert P <= 1 and P >= 0 | |
| logits = model(**tokenizer(text, return_tensors="pt")).logits | |
| probabilities = torch.softmax(logits, dim=1).flatten().tolist() | |
| topk_predictions = torch.topk(logits, 18).indices.flatten().tolist() | |
| predictions = [0 for _ in range(18)] | |
| total_prob = 0 | |
| for i in range(18): | |
| total_prob += probabilities[topk_predictions[i]] | |
| predictions[topk_predictions[i]] = 1 | |
| if total_prob >= P: | |
| break | |
| return [DIALECTS[i] for i, p in enumerate(predictions) if p == 1] | |